def receive():
f = request.form
beds = float(f['beds'])
baths = float(f['baths'])
address = str(f['address']) + ' ' + str(f['zip'])
neighborhood = str(f['neigh'])
parking = float(f['parking'])
description = str(f['desc'])
price = int(f['price'])
predict_statement, compare_statement = run_pmr(beds, baths, address, neighborhood, parking,
description, price, app.rfr,
app.search_df, app.nhood_medians,
app.vectorizer, app.nmf, app.kd)
# Your code here to process data
return render_template('result.html', prediction=predict_statement,
comparison=compare_statement)
from pricemyrental import PriceMyRental, load_data_and_models, run_pmr
'''
Test the PriceMyRental pipeline
'''
beds = 1
baths = 2
neighborhood = 'mission district'
address = '350 Alabama St, San Francisco CA, 94110'
parking = 4 #Attached garage
description = 'This beautiful Contemporary 1017 square foot condo home has 1 bedrooms and \
2.0 bathrooms in the vibrant and hip Inner Mission is a light-filled oasis \
with views to the west and Twin Peaks. The main level features an open \
floor plan with soaring ceilings and a wall of windows. The kitchen is \
adjacent to the living and dining area and includes stainless steel \
appliances, granite counters, and ceramic tile backsplash. A walk-out \
balcony and a powder room with w/d hook-ups complete this level. Upstairs\
is the mezzanine bedroom with a full bathroom and custom closet system. \
One car parking and storage room complete this home in the middle of an \
active neighborhood. Easy access to 101/280, public transportation and shuttle stops.'
price = 3000
if __name__=='__main__':
rfr, search_df, nhood_medians, vectorizer, nmf, kd = load_data_and_models()
print run_pmr(beds, baths, address, neighborhood, parking,
description, price, rfr, search_df,
nhood_medians, vectorizer, nmf, kd)